demos of neural networks, in python
The toynn.py script is the main script, it generates data from a toy classification problem and trains a multilayered neural network to solve it.
The network is built using the code in layers.py, which contains two classes: a Network class which contains a stack of Layers. Each layer implements one of the layers in the network, and keeps track of the parameters of that layer. the Network connects these layers together so that you can compute forward or backward passes through the network.
The Layer class lets you specify the size (# of input and output dimensions) for each layer. The nonlinearity is assumed to be a sigmoid (see the function in utils.py) and the parameters are optimized using adam.
The toy classification problem consists of trying to classify points where the decision boundary is given by the 2D boundary of a norm ball.
$ git clone https://github.com/nirum/deep-learning-demos
$ cd deep-learning-demos
$ pip install -r requirements.txt